{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import gzip\n",
    "import os\n",
    "% matplotlib inline\n",
    "plt.ion()\n",
    "\n",
    "import sys\n",
    "sys.path.append('../')\n",
    "import logomaker"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def write_df_and_description(df, description, file_name, index=False, feedback=True):\n",
    "    \"\"\" Write a data frame, with comment, to file\"\"\"\n",
    "    \n",
    "    # convert description to comment\n",
    "    comment = '\\n'.join(['# %s'%line.strip() for line in description.split('\\n')])\n",
    "\n",
    "    # remove file if it already exists\n",
    "    if os.path.isfile(file_name):\n",
    "        os.remove(file_name)        \n",
    "    \n",
    "    # open file for appending\n",
    "    with open(file_name,'a') as f:\n",
    "        \n",
    "        # write comment\n",
    "        f.write(comment+'\\n')\n",
    "        \n",
    "        # write data frame\n",
    "        df.to_csv(f, sep='\\t', index=index)\n",
    "    \n",
    "    # provide feedback if desired\n",
    "    if feedback:\n",
    "        # show file name and location\n",
    "        print('-> saving data to %s:'%file_name)\n",
    "              \n",
    "        # preview file\n",
    "        with open(file_name,'r') as f:\n",
    "            lines = f.readlines()\n",
    "            head_length = min(len(lines),15)\n",
    "            tail_length = min(len(lines),10)\n",
    "            print(''.join(lines[:head_length]) + '...\\n' + ''.join(lines[-tail_length:]))\n",
    "            \n",
    "    # compress, read in then write compressed file\n",
    "    \n",
    "    if file_name[-3:]=='.gz':\n",
    "\n",
    "        # read in non-compressed file\n",
    "        print('Compressing file...')\n",
    "        with open(file_name,'rb') as f:\n",
    "            tmp_file_name = 'tmp.gz'\n",
    "            with gzip.open(tmp_file_name, 'wb') as g:\n",
    "                g.writelines(f)\n",
    "                \n",
    "        # move tmp file to final file\n",
    "        os.rename(tmp_file_name, file_name)\n",
    "        print('Done!')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def write_txt_and_description(txt, description, file_name, index=False, feedback=True):\n",
    "    \"\"\" Write a data frame, with comment, to file\"\"\"\n",
    "    \n",
    "    # convert description to comment\n",
    "    comment = '\\n'.join(['# %s'%line.strip() for line in description.split('\\n')])\n",
    "\n",
    "    # remove file if it already exists\n",
    "    if os.path.isfile(file_name):\n",
    "        os.remove(file_name)        \n",
    "    \n",
    "    # open file for appending\n",
    "    with open(file_name,'a') as f:\n",
    "        \n",
    "        # write comment\n",
    "        f.write(comment+'\\n')\n",
    "        \n",
    "        # write data frame\n",
    "        f.write(txt)\n",
    "    \n",
    "    # provide feedback if desired\n",
    "    if feedback:\n",
    "        # show file name and location\n",
    "        print('-> saving data to %s:'%file_name)\n",
    "              \n",
    "        # preview file\n",
    "        with open(file_name,'r') as f:\n",
    "            lines = f.readlines()\n",
    "            head_length = min(len(lines),15)\n",
    "            tail_length = min(len(lines),10)\n",
    "            print(''.join(lines[:head_length]) + '...\\n' + ''.join(lines[-tail_length:]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Set output directories\n",
    "data_dir = '../logomaker/examples/datafiles/'\n",
    "mat_dir = '../logomaker/examples/matrices/'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Saliency data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-> saving data to ../logomaker/examples/datafiles/nn_saliency_values.txt:\n",
      "# \n",
      "# Saliency values illustrated in Figure 1F.\n",
      "# Data are from Figure 1D of Janganathan et al. (2019),\n",
      "# and were kindly provided by Kyle Farh and Kishore Jaganathan.\n",
      "# \n",
      "# References:\n",
      "# \n",
      "# Jaganathan K et al. (2019) Predicting Splicing from Primary Sequence with\n",
      "# Deep Learning. Cell. 176(3):535–548.e24.\n",
      "# \n",
      "character\tvalue\n",
      "G\t-0.0017247200012207031\n",
      "G\t0.03355717658996582\n",
      "G\t0.030026257038116455\n",
      "G\t0.012748241424560547\n",
      "...\n",
      "A\t-0.004249751567840576\n",
      "T\t0.019003868103027344\n",
      "A\t-0.00032633543014526367\n",
      "A\t-0.010485649108886719\n",
      "A\t0.0017966628074645996\n",
      "T\t0.021005749702453613\n",
      "T\t0.019015133380889893\n",
      "T\t0.010700225830078125\n",
      "T\t0.010440587997436523\n",
      "C\t-0.01064610481262207\n",
      "\n"
     ]
    }
   ],
   "source": [
    "### Format saliency data\n",
    "\n",
    "# write description\n",
    "description =  \"\"\"\n",
    "Saliency values illustrated in Figure 1F.\n",
    "Data are from Figure 1D of Janganathan et al. (2019), \n",
    "and were kindly provided by Kyle Farh and Kishore Jaganathan. \n",
    "\n",
    "References:\n",
    "\n",
    "Jaganathan K et al. (2019) Predicting Splicing from Primary Sequence with \n",
    "Deep Learning. Cell. 176(3):535–548.e24. \n",
    "\"\"\"\n",
    "\n",
    "# load saliency data \n",
    "data = np.load('importance_score.npz')\n",
    "tmp_df = pd.DataFrame(data=data['arr_0'].T, columns=list('ACGT'))\n",
    "\n",
    "# format saliency data\n",
    "saliency_data_df = pd.DataFrame()\n",
    "for i, row in tmp_df.iterrows():\n",
    "    abs_vals = np.abs(row.values)\n",
    "    col_num = np.argmax(abs_vals)\n",
    "    saliency_data_df.loc[i,'character'] = tmp_df.columns[col_num]\n",
    "    saliency_data_df.loc[i,'value'] = row.iloc[col_num]\n",
    "\n",
    "# write data and description, then show file\n",
    "write_df_and_description(df=saliency_data_df, \n",
    "                         description=description,\n",
    "                         file_name=data_dir+'nn_saliency_values.txt')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Saliency matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-> saving data to ../logomaker/examples/matrices/nn_saliency_matrix.txt:\n",
      "# \n",
      "# Saliency matrix illustrated in Figure 1F.\n",
      "# Data are from Figure 1D of Janganathan et al. (2019),\n",
      "# and were kindly provided by Kyle Farh and Kishore Jaganathan.\n",
      "# \n",
      "# References:\n",
      "# \n",
      "# Jaganathan K et al. (2019) Predicting Splicing from Primary Sequence with\n",
      "# Deep Learning. Cell. 176(3):535–548.e24.\n",
      "# \n",
      "pos\tA\tC\tG\tT\n",
      "0\t-0.0\t-0.0\t-0.0017247200012207033\t-0.0\n",
      "1\t0.0\t0.0\t0.03355717658996582\t0.0\n",
      "2\t0.0\t0.0\t0.03002625703811645\t0.0\n",
      "3\t0.0\t0.0\t0.012748241424560549\t0.0\n",
      "...\n",
      "119\t-0.004249751567840576\t-0.0\t-0.0\t-0.0\n",
      "120\t0.0\t0.0\t0.0\t0.019003868103027344\n",
      "121\t-0.00032633543014526367\t-0.0\t-0.0\t-0.0\n",
      "122\t-0.01048564910888672\t-0.0\t-0.0\t-0.0\n",
      "123\t0.0017966628074645996\t0.0\t0.0\t0.0\n",
      "124\t0.0\t0.0\t0.0\t0.021005749702453613\n",
      "125\t0.0\t0.0\t0.0\t0.01901513338088989\n",
      "126\t0.0\t0.0\t0.0\t0.010700225830078123\n",
      "127\t0.0\t0.0\t0.0\t0.010440587997436523\n",
      "128\t-0.0\t-0.01064610481262207\t-0.0\t-0.0\n",
      "\n"
     ]
    }
   ],
   "source": [
    "### Compute saliency matrix\n",
    "\n",
    "description = \"\"\"\n",
    "Saliency matrix illustrated in Figure 1F.\n",
    "Data are from Figure 1D of Janganathan et al. (2019), \n",
    "and were kindly provided by Kyle Farh and Kishore Jaganathan. \n",
    "\n",
    "References:\n",
    "\n",
    "Jaganathan K et al. (2019) Predicting Splicing from Primary Sequence with \n",
    "Deep Learning. Cell. 176(3):535–548.e24. \n",
    "\"\"\"\n",
    "\n",
    "# load saliency data\n",
    "with logomaker.open_example_datafile('nn_saliency_values.txt', print_description=False) as f:\n",
    "    saliency_data_df = pd.read_csv(f, comment='#', sep='\\t')\n",
    "\n",
    "# create saliency matrix\n",
    "saliency_mat_df = logomaker.saliency_to_matrix(seq=saliency_data_df['character'], \n",
    "                                               values=saliency_data_df['value'])\n",
    "\n",
    "# write data and description, then show file\n",
    "write_df_and_description(df=saliency_mat_df, \n",
    "                         description=description,\n",
    "                         file_name=mat_dir+'nn_saliency_matrix.txt',\n",
    "                         index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Description of example matrix \"nn_saliency_matrix\":\n",
      "# \n",
      "# Saliency matrix illustrated in Figure 1F.\n",
      "# Data are from Figure 1D of Janganathan et al. (2019),\n",
      "# and were kindly provided by Kyle Farh and Kishore Jaganathan.\n",
      "# \n",
      "# References:\n",
      "# \n",
      "# Jaganathan K et al. (2019) Predicting Splicing from Primary Sequence with\n",
      "# Deep Learning. Cell. 176(3):535–548.e24.\n",
      "# \n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<logomaker.src.Logo.Logo at 0x114e12c50>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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q1JhTAiCeStAT6+GX636ZLrfQE+vJeSTUWFBBPJqIMr15+pg4VvWhes8bHrfM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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x180 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Validate saliency matrix\n",
    "df = logomaker.get_example_matrix('nn_saliency_matrix')\n",
    "logomaker.Logo(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CRP sites data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-> saving data to ../logomaker/examples/datafiles/crp_sites.fa:\n",
      "# \n",
      "# CRP binding sites from RegulonDB, in FASTA format.\n",
      "# \n",
      "# References\n",
      "# \n",
      "# Salgado H, et al. (2013) RegulonDB v8.0: omics data sets, evolutionary\n",
      "# conservation, regulatory phrases, cross-validated gold standards\n",
      "# and more. Nucl Acids Res. 41(Database issue):D203–13.\n",
      "# \n",
      "# \n",
      ">0\tcaiFp\t-41.5\n",
      "ATAAGCAGGATTTAGCTCACACTTAT\n",
      ">1\tcaiTp\t-41.5\n",
      "AAAAATGTGATACCAATCACAGAATA\n",
      ">2\tfixAp\t-126.5\n",
      "...\n",
      ">353\tidnDp\t-41.5\n",
      "AAAATTGTGATCTATATTTAACAAAG\n",
      ">354\tidnKp\t-70.5\n",
      "ATATTTAACAAAGTGATGACATTTCT\n",
      ">355\tidnDp\t-91.5\n",
      "AGTGATGACATTTCTGACGGCGTTAA\n",
      ">356\tnanCp\t-61.5\n",
      "AATTATTTGAACCAGATCGCATTACA\n",
      ">357\tnanCp\t-61.5\n",
      "TTAATTGTGATGTGTATCGAAGTGTG\n",
      "\n"
     ]
    }
   ],
   "source": [
    "### Format CRP sites data\n",
    "\n",
    "description = \"\"\"\n",
    "CRP binding sites from RegulonDB, in FASTA format.\n",
    "\n",
    "References\n",
    "\n",
    "Salgado H, et al. (2013) RegulonDB v8.0: omics data sets, evolutionary \n",
    "conservation, regulatory phrases, cross-validated gold standards \n",
    "and more. Nucl Acids Res. 41(Database issue):D203–13. \n",
    "\n",
    "\"\"\"\n",
    "\n",
    "# load fasta file as txt\n",
    "with open('crp_sites.fasta') as f:\n",
    "    txt = f.read()\n",
    "    \n",
    "# write back as text with description header\n",
    "write_txt_and_description(txt, description, data_dir+'crp_sites.fa')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CRP counts matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-> saving data to ../logomaker/examples/matrices/crp_counts_matrix.txt:\n",
      "# \n",
      "# CRP counts matrix. Created from the CRP binding sites from listed in RegulonDB.\n",
      "# \n",
      "# References\n",
      "# \n",
      "# Salgado H, et al. (2013) RegulonDB v8.0: omics data sets, evolutionary\n",
      "# conservation, regulatory phrases, cross-validated gold standards\n",
      "# and more. Nucl Acids Res. 41(Database issue):D203–13.\n",
      "# \n",
      "# \n",
      "pos\tA\tC\tG\tT\n",
      "0\t133.0\t65.0\t72.0\t88.0\n",
      "1\t147.0\t46.0\t58.0\t107.0\n",
      "2\t166.0\t26.0\t38.0\t128.0\n",
      "3\t164.0\t28.0\t43.0\t123.0\n",
      "...\n",
      "16\t33.0\t31.0\t31.0\t263.0\n",
      "17\t46.0\t261.0\t15.0\t36.0\n",
      "18\t250.0\t30.0\t44.0\t34.0\n",
      "19\t62.0\t244.0\t16.0\t36.0\n",
      "20\t231.0\t36.0\t46.0\t45.0\n",
      "21\t97.0\t68.0\t51.0\t142.0\n",
      "22\t120.0\t40.0\t20.0\t178.0\n",
      "23\t116.0\t44.0\t24.0\t174.0\n",
      "24\t90.0\t54.0\t40.0\t174.0\n",
      "25\t103.0\t62.0\t60.0\t133.0\n",
      "\n"
     ]
    }
   ],
   "source": [
    "### Create CRP counts matrix from FASTA file\n",
    "\n",
    "description = \"\"\"\n",
    "CRP counts matrix. Created from the CRP binding sites from listed in RegulonDB.\n",
    "\n",
    "References\n",
    "\n",
    "Salgado H, et al. (2013) RegulonDB v8.0: omics data sets, evolutionary \n",
    "conservation, regulatory phrases, cross-validated gold standards \n",
    "and more. Nucl Acids Res. 41(Database issue):D203–13. \n",
    "\n",
    "\"\"\"\n",
    "\n",
    "# load fasta of CRP binding sites\n",
    "with logomaker.open_example_datafile('crp_sites.fa', print_description=False) as f:\n",
    "    lines = f.readlines()\n",
    "    \n",
    "# extract binding site sequences\n",
    "seqs = [seq.strip() for seq in lines if ('#' not in seq) and ('>') not in seq]\n",
    "\n",
    "# create counts matrix\n",
    "crp_counts_df = logomaker.alignment_to_matrix(sequences=seqs, to_type='counts')\n",
    "\n",
    "# write counts matrix to file\n",
    "write_df_and_description(df=crp_counts_df, \n",
    "                         description=description, \n",
    "                         file_name=mat_dir+'crp_counts_matrix.txt',\n",
    "                         index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Description of example matrix \"crp_counts_matrix\":\n",
      "# \n",
      "# CRP counts matrix. Created from the CRP binding sites from listed in RegulonDB.\n",
      "# \n",
      "# References\n",
      "# \n",
      "# Salgado H, et al. (2013) RegulonDB v8.0: omics data sets, evolutionary\n",
      "# conservation, regulatory phrases, cross-validated gold standards\n",
      "# and more. Nucl Acids Res. 41(Database issue):D203–13.\n",
      "# \n",
      "# \n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<logomaker.src.Logo.Logo at 0x114e12978>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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GQaZkzSiykixVXxXMClVFXoXFZwLCU51k5GlqwVSmFky19Et4mYZ45huxIy7pE+D8t2DK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oj9M50RPVTxo0gfy9/HX5WJbBvaVhFkWWUr1mSswdiuYx1vEi0iDHp1BpP2awAgW3DggDbn8777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\nF1544YUXXjQB/h877pi0CmX83gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x180 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# validate crp counts matrix\n",
    "df = logomaker.get_example_matrix('crp_counts_matrix')\n",
    "logomaker.Logo(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CRP energy matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-> saving data to ../logomaker/examples/matrices/crp_energy_matrix.txt:\n",
      "# \n",
      "# CRP energy matrix from Kinney et al. (2010).\n",
      "# Matrix values are in units of kcal/mol.\n",
      "# \n",
      "# References\n",
      "# \n",
      "# Kinney JB et al. (2010) Using deep sequencing to characterize the\n",
      "# biophysical mechanism of a transcriptional regulatory sequence.\n",
      "# Proc Natl Acad Sci USA. 107(20):9158–63.\n",
      "# \n",
      "pos\tA\tC\tG\tT\n",
      "0\t-0.18364197530864196\t0.15586419753086422\t0.09413580246913582\t-0.06635802469135801\n",
      "1\t-0.29012345679012347\t0.2777777777777778\t0.1111111111111111\t-0.09876543209876544\n",
      "2\t-0.2762345679012346\t0.316358024691358\t0.16820987654320985\t-0.20833333333333334\n",
      "3\t-0.22685185185185186\t0.2854938271604938\t0.2978395061728395\t-0.35648148148148145\n",
      "...\n",
      "16\t0.4444444444444444\t0.3024691358024691\t-0.049382716049382755\t-0.6975308641975309\n",
      "17\t0.08024691358024698\t-1.0987654320987652\t0.6111111111111112\t0.4074074074074075\n",
      "18\t-0.5864197530864197\t0.3703703703703704\t0.17901234567901236\t0.03703703703703707\n",
      "19\t-0.17438271604938277\t-0.6867283950617283\t0.677469135802469\t0.18364197530864187\n",
      "20\t-0.8009259259259259\t0.5447530864197531\t-0.17746913580246917\t0.4336419753086419\n",
      "21\t-0.220679012345679\t0.2237654320987654\t0.057098765432098776\t-0.06018518518518517\n",
      "22\t-0.06635802469135797\t0.17438271604938269\t0.4274691358024692\t-0.5354938271604938\n",
      "23\t-0.14660493827160495\t0.14969135802469136\t0.24845679012345678\t-0.2515432098765432\n",
      "24\t0.1574074074074074\t-0.08333333333333333\t0.24382716049382716\t-0.3179012345679012\n",
      "25\t0.12191358024691354\t0.01697530864197532\t0.09104938271604938\t-0.22993827160493827\n",
      "\n"
     ]
    }
   ],
   "source": [
    "### Format CRP energy matrix\n",
    "\n",
    "# write description\n",
    "description = \"\"\"\n",
    "CRP energy matrix from Kinney et al. (2010). \n",
    "Matrix values are in units of kcal/mol. \n",
    "\n",
    "References\n",
    "\n",
    "Kinney JB et al. (2010) Using deep sequencing to characterize the \n",
    "biophysical mechanism of a transcriptional regulatory sequence. \n",
    "Proc Natl Acad Sci USA. 107(20):9158–63. \n",
    "\"\"\"\n",
    "\n",
    "# load published energy matrix\n",
    "energy_df = pd.read_csv('crp_tau_final_all.26.txt', delim_whitespace=True, index_col=0)\n",
    "energy_df.columns = [c[-1] for c in energy_df.columns]\n",
    "energy_df = logomaker.transform_matrix(energy_df, center_values=True)\n",
    "energy_df = energy_df/1.62   # convert to kcal/mol\n",
    "\n",
    "# save energy matrix with description\n",
    "write_df_and_description(df=energy_df,\n",
    "                         description=description,\n",
    "                         file_name=mat_dir+'crp_energy_matrix.txt',\n",
    "                         index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Description of example matrix \"crp_energy_matrix\":\n",
      "# \n",
      "# CRP energy matrix from Kinney et al. (2010).\n",
      "# Matrix values are in units of kcal/mol.\n",
      "# \n",
      "# References\n",
      "# \n",
      "# Kinney JB et al. (2010) Using deep sequencing to characterize the\n",
      "# biophysical mechanism of a transcriptional regulatory sequence.\n",
      "# Proc Natl Acad Sci USA. 107(20):9158–63.\n",
      "# \n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<logomaker.src.Logo.Logo at 0x115408ef0>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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F4p8lhzMOU+xyzp6RPekR2UO17+7k3dzU6yaPXt/AwB16JSUaQnztSd5Dib0Eb7P2wigw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\nQtRI9Xj8NyLDSA/vUOj6GLGx4uGv9Sf6/ntRtPJ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qkPVAFi2jy3WzDnwLi0c5n9Tlb3Ce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\nTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x180 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# validate crp energy matrix\n",
    "df = logomaker.get_example_matrix('crp_energy_matrix')\n",
    "logomaker.Logo(-df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# WW domain sequences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-> saving data to ../logomaker/examples/datafiles/ww_sequences.fa:\n",
      "# \n",
      "# WW domain alignment in FASTA format.\n",
      "# From PFAM, RP15 (4025)\n",
      "# http://pfam.xfam.org/family/PF00397#tabview=tab3\n",
      "# \n",
      "# References\n",
      "# \n",
      "# Finn RD et al. (2014) Pfam: the protein families database.\n",
      "# Nucl Acids Res. 42(Database issue):D222–30.\n",
      "# \n",
      ">B7G9D5/250-280\n",
      "LPPQW..TEA.VDVDT...GKFYFVHVET.......KETRWERP\n",
      ">B7G9D5/328-356\n",
      "--PGW..TAT.VDPAS...GRTYYYHAAT.......GETRWEPP\n",
      ">B7G9D5/387-417\n",
      "...\n",
      ">A0A0D0AUR6/106-128\n",
      "-----..--H.ISPL-...GRSYFVNHNT.......RTTSWKKP\n",
      ">A0A0D0B2I1/93-115\n",
      "-----..--Y.ISPL-...GRSYFVNHNT.......RTTSWKKP\n",
      ">A0A0B2W0M5/472-501\n",
      "LPAGW..EKH.QDPS-...GYSYYWHVDS.......GTIQREPP\n",
      ">A0A1B7MQV1/109-126\n",
      "-----..---.-----...GRSYFVNHNT.......RTTSWKKP\n",
      ">A0A0D0APJ1/108-130\n",
      "-----..--H.ISPL-...GRSYFVNHNT.......KTTSWRKP\n"
     ]
    }
   ],
   "source": [
    "### Fromat WW domain alignment\n",
    "\n",
    "description = \"\"\"\n",
    "WW domain alignment in FASTA format.\n",
    "From PFAM, RP15 (4025)\n",
    "http://pfam.xfam.org/family/PF00397#tabview=tab3\n",
    "\n",
    "References\n",
    "\n",
    "Finn RD et al. (2014) Pfam: the protein families database. \n",
    "Nucl Acids Res. 42(Database issue):D222–30. \n",
    "\"\"\"\n",
    "\n",
    "# load fasta file as txt\n",
    "with open('Pfam_wwdomain.fa') as f:\n",
    "    lines = f.readlines()\n",
    "    \n",
    "# Remove comments\n",
    "lines = [line.strip() for line in lines if '#' not in line and len(line.strip())>0]\n",
    "txt = '\\n'.join(lines)\n",
    "    \n",
    "# write back as text with description header\n",
    "write_txt_and_description(txt, description, data_dir+'ww_sequences.fa')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# WW information matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-> saving data to ../logomaker/examples/matrices/ww_information_matrix.txt:\n",
      "# \n",
      "# WW domain information matrix (in bits).\n",
      "# Counts derived From PFAM, RP15 (4025)\n",
      "# http://pfam.xfam.org/family/PF00397#tabview=tab3\n",
      "# Note: Positions with gaps in at least half of the\n",
      "# sequences were removed.\n",
      "# \n",
      "# References\n",
      "# \n",
      "# Finn RD et al. (2014) Pfam: the protein families database.\n",
      "# Nucl Acids Res. 42(Database issue):D222–30.\n",
      "# \n",
      "pos\tA\tC\tD\tE\tF\tG\tH\tI\tK\tL\tM\tN\tP\tQ\tR\tS\tT\tV\tW\tY\n",
      "0\t0.001591526468795453\t0.001591526468795453\t0.001591526468795453\t0.001591526468795453\t0.001591526468795453\t0.001591526468795453\t0.001591526468795453\t0.001591526468795453\t0.001591526468795453\t4.013829754302133\t0.001591526468795453\t0.001591526468795453\t0.001591526468795453\t0.001591526468795453\t0.001591526468795453\t0.001591526468795453\t0.001591526468795453\t0.06843563815820448\t0.001591526468795453\t0.001591526468795453\n",
      "1\t0.03497481914499014\t0.0013989927657996053\t0.0013989927657996053\t0.020984891486994083\t0.006994963828998028\t0.012590934892196452\t0.013989927657996056\t0.0013989927657996053\t0.006994963828998028\t0.012590934892196452\t0.0013989927657996053\t0.012590934892196452\t3.710128814900554\t0.022383884252793685\t0.004196978297398817\t0.013989927657996056\t0.004196978297398817\t0.0027979855315992106\t0.0013989927657996053\t0.0013989927657996053\n",
      "...\n",
      "20\t0.054469278148659726\t0.0006960930114844695\t0.013399790471076036\t0.05986399898766436\t0.011137488183751511\t0.005046674333262403\t0.00922323240216922\t0.057253650194597605\t0.04629018526371721\t0.033064418045512294\t0.0041765580689068155\t0.18028808997447754\t0.00974530216078257\t0.03724097611441911\t0.040721441171841455\t0.03567476683857906\t0.03289039479264118\t0.0612561850106333\t0.00034804650574223473\t0.010615418425138157\n",
      "21\t0.06243471159696421\t0.0019925971786265173\t0.03653094827481949\t0.05512852194200032\t0.0033209952977108622\t0.00265679623816869\t0.015940777429012138\t0.005977791535879552\t0.018597573667180827\t0.03254575391756645\t0.004649393416795207\t0.24708205014968815\t0.0006641990595421725\t0.015940777429012138\t0.02391116614351821\t0.22782027742296515\t1.9228562773745892\t0.0006641990595421725\t0.0006641990595421725\t0.0006641990595421725\n",
      "22\t0.007350862415053444\t0.006125718679211204\t0.022869349735721828\t0.05023089316953187\t0.001633524981122988\t0.34099833980942373\t0.02123582475459884\t0.000408381245280747\t0.4275751638089421\t0.006942481169772698\t0.009392768641457182\t0.1053623612824327\t0.01756039354707212\t0.12128922984838186\t0.4880155881104926\t0.018377156037633617\t0.0028586687169652285\t0.000408381245280747\t0.000408381245280747\t0.001633524981122988\n",
      "23\t0.05224182717328452\t0.0030730486572520307\t0.010584945374979216\t0.3359866531928887\t0.0013657994032231248\t0.0027315988064462496\t0.010584945374979216\t0.035510784483801246\t0.08911841106030888\t0.01980409134673531\t0.004438848060475156\t0.005121747762086718\t0.0006828997016115624\t0.12292194629008123\t0.07341171792324296\t0.07102156896760249\t0.43773870873301146\t0.10106915583851124\t0.0003414498508057812\t0.0013657994032231248\n",
      "24\t0.027864212658158884\t0.010717004868522648\t0.002857867964939373\t0.0014289339824696864\t0.0014289339824696864\t0.0021434009737045295\t0.0007144669912348432\t0.011431471859757491\t0.007144669912348432\t0.0021434009737045295\t0.003572334956174216\t0.014289339824696864\t0.005715735929878746\t0.004286801947409059\t0.022862943719514982\t0.8745075972714481\t1.8004568179118048\t0.0893083739043554\t0.0007144669912348432\t0.0021434009737045295\n",
      "25\t0.019967110655179008\t0.020431462065764565\t0.001393054231756675\t0.0009287028211711167\t0.001393054231756675\t0.00046435141058555834\t0.05479346644909588\t0.01903840783400789\t0.02182451629752124\t0.029254138866890173\t0.004643514105855583\t0.0009287028211711167\t0.00046435141058555834\t0.5697591807884801\t0.06872400876666263\t0.26235854698084043\t0.6923479531830674\t0.10215731032882282\t0.00046435141058555834\t0.0023217570529277913\n",
      "26\t0.0009068065482514607\t0.003627226193005843\t0.0009068065482514607\t0.0009068065482514607\t0.12423249711045012\t0.0009068065482514607\t0.0009068065482514607\t0.009068065482514608\t0.0009068065482514607\t0.013602098223771912\t0.0009068065482514607\t0.0018136130965029215\t0.0009068065482514607\t0.0009068065482514607\t0.014508904772023372\t0.0009068065482514607\t0.0018136130965029215\t0.0027204196447543822\t3.274478445736025\t0.1940566013258126\n",
      "27\t0.009927690366530337\t0.0013236920488707115\t0.2154308809537083\t0.501348363509782\t0.009265844342094981\t0.002647384097741423\t0.026804763989631906\t0.05162398990595774\t0.04434368363716883\t0.04401276062495116\t0.0016546150610883895\t0.05294768195482846\t0.0003309230122176779\t0.07875967690780733\t0.008934921329877303\t0.03243045519733243\t0.1538792006812202\t0.07644321582228358\t0.0003309230122176779\t0.012244151452054082\n",
      "28\t0.019567082389432146\t0.006627560164162501\t0.3004493941087001\t0.0053651677519410725\t0.018935886183321433\t0.0003155981030553572\t0.13539158621074826\t0.018304689977210718\t0.20766355181042503\t0.036609379954421435\t0.02840382927498215\t0.032191006511646435\t0.10193818728688038\t0.006943158267217859\t0.2957154225628697\t0.008521148782494645\t0.005049569648885715\t0.02146067100776429\t0.0003155981030553572\t0.004102775339719643\n",
      "29\t0.0010757078639578542\t0.0010757078639578542\t0.0010757078639578542\t0.0010757078639578542\t0.0010757078639578542\t0.0010757078639578542\t0.0010757078639578542\t0.0010757078639578542\t0.0010757078639578542\t0.0010757078639578542\t0.0010757078639578542\t0.0010757078639578542\t4.237213276129988\t0.0010757078639578542\t0.0010757078639578542\t0.0010757078639578542\t0.0010757078639578542\t0.0010757078639578542\t0.0010757078639578542\t0.0010757078639578542\n",
      "\n"
     ]
    }
   ],
   "source": [
    "### Format WW domain sequences\n",
    "\n",
    "description = \"\"\"\n",
    "WW domain information matrix (in bits). \n",
    "Counts derived From PFAM, RP15 (4025)\n",
    "http://pfam.xfam.org/family/PF00397#tabview=tab3\n",
    "Note: Positions with gaps in at least half of the \n",
    "sequences were removed. \n",
    "\n",
    "References\n",
    "\n",
    "Finn RD et al. (2014) Pfam: the protein families database. \n",
    "Nucl Acids Res. 42(Database issue):D222–30. \n",
    "\"\"\"\n",
    "\n",
    "# read in fasta file\n",
    "with logomaker.open_example_datafile('ww_sequences.fa', print_description=False) as f:\n",
    "    lines = f.readlines()\n",
    "\n",
    "# extract ww domain sequences\n",
    "seqs = [seq.strip().upper() for seq in lines if ('#' not in seq) and ('>') not in seq]\n",
    "\n",
    "# create counts matrix\n",
    "ww_counts_df = logomaker.alignment_to_matrix(sequences=seqs, to_type='counts', characters_to_ignore='.-X')\n",
    "\n",
    "# filter base on counts\n",
    "num_seqs = ww_counts_df.sum(axis=1)\n",
    "pos_to_keep = num_seqs > len(seqs)/2\n",
    "ww_counts_df = ww_counts_df[pos_to_keep]\n",
    "ww_counts_df.reset_index(drop=True, inplace=True)\n",
    "\n",
    "# transform to information matrix\n",
    "ww_info_df = logomaker.transform_matrix(ww_counts_df, from_type='counts', to_type='information')\n",
    "\n",
    "# write counts matrix to file\n",
    "write_df_and_description(df=ww_info_df, \n",
    "                         description=description, \n",
    "                         file_name=mat_dir+'ww_information_matrix.txt',\n",
    "                         index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Description of example matrix \"ww_information_matrix\":\n",
      "# \n",
      "# WW domain information matrix (in bits).\n",
      "# Counts derived From PFAM, RP15 (4025)\n",
      "# http://pfam.xfam.org/family/PF00397#tabview=tab3\n",
      "# Note: Positions with gaps in at least half of the\n",
      "# sequences were removed.\n",
      "# \n",
      "# References\n",
      "# \n",
      "# Finn RD et al. (2014) Pfam: the protein families database.\n",
      "# Nucl Acids Res. 42(Database issue):D222–30.\n",
      "# \n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<logomaker.src.Logo.Logo at 0x115433940>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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CbdsC7dqRxbddrKDSbKz1NLmqaBoZN9LqtKPL3XXlEjlGNh9ps/OXK8t5Y0J+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\n",
      "text/plain": [
       "<Figure size 720x180 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# validate ww domain file\n",
    "df = logomaker.get_example_matrix('ww_information_matrix')\n",
    "logomaker.Logo(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5'ss sequences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-> saving data to ../logomaker/examples/datafiles/ss_sequences.txt:\n",
      "# \n",
      "# 5'ss sequences in the human genome.\n",
      "# The number of times each 5'ss occurs in the genome is also provided.\n",
      "# Sequences were parsed from hg38 using GENCODE annotation.\n",
      "# Only sequences of the form NNNGYNNNN are listed.\n",
      "# \n",
      "# References\n",
      "# \n",
      "# Frankish A et al. (2019) GENCODE reference annotation\n",
      "# for the human and mouse genomes. Nucl Acids Res. 47(D1):D766–73.\n",
      "# \n",
      "sequence\tcount\n",
      "CAGGUGAGU\t3729\n",
      "CAGGUGAGG\t3653\n",
      "CAGGUGAGC\t3090\n",
      "...\n",
      "GCUGUAACA\t1\n",
      "GCUGCUGGC\t1\n",
      "GCUGUAAAC\t1\n",
      "GCUGUAAAA\t1\n",
      "GCUGCUUUG\t1\n",
      "GCUGCUUGG\t1\n",
      "CAAGUAGCG\t1\n",
      "CAAGUAGUG\t1\n",
      "CAAGUAGUU\t1\n",
      "CUGGUUCUC\t1\n",
      "\n"
     ]
    }
   ],
   "source": [
    "### Format 5' ss sequences\n",
    "\n",
    "# set file header\n",
    "description = \"\"\"\n",
    "5'ss sequences in the human genome. \n",
    "The number of times each 5'ss occurs in the genome is also provided.\n",
    "Sequences were parsed from hg38 using GENCODE annotation. \n",
    "Only sequences of the form NNNGYNNNN are listed.  \n",
    "\n",
    "References\n",
    "\n",
    "Frankish A et al. (2019) GENCODE reference annotation \n",
    "for the human and mouse genomes. Nucl Acids Res. 47(D1):D766–73. \n",
    "\"\"\"\n",
    "\n",
    "# load all splice sites\n",
    "tmp1_df = pd.read_csv('hg38_sss.txt.gz', sep='\\t', index_col=0)\n",
    "\n",
    "# get 5' splice sites\n",
    "seqs = [s.replace('T','U') for s in tmp1_df['ss5_seq'] if s[3:5] in {'GC','GT'}]\n",
    "tmp2_df = pd.DataFrame(data=seqs, columns=['sequence'])\n",
    "\n",
    "# uniquify sequence list\n",
    "tmp2_df['count'] = 1\n",
    "ss_seqs_df = tmp2_df.groupby('sequence').sum()\n",
    "ss_seqs_df.sort_values(by='count', ascending=False, inplace=True)\n",
    "ss_seqs_df.reset_index(inplace=True)\n",
    "\n",
    "# write to file\n",
    "write_df_and_description(df=ss_seqs_df,\n",
    "                         description=description,\n",
    "                         file_name=data_dir+'ss_sequences.txt')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5'ss probability matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-> saving data to ss_probability_matrix.txt:\n",
      "# \n",
      "# Probability matrix for all 5'ss sequences in the human genome.\n",
      "# Sequences were parsed from hg38 using GENCODE annotation.\n",
      "# Only sequences of the form NNNGYNNNN were considered.\n",
      "# \n",
      "# References\n",
      "# \n",
      "# Frankish A et al. (2019) GENCODE reference annotation\n",
      "# for the human and mouse genomes. Nucl Acids Res. 47(D1):D766–73.\n",
      "# \n",
      "pos\tA\tC\tG\tU\n",
      "0\t0.32465600595016736\t0.3589800287457409\t0.18931315100894872\t0.12705081429514303\n",
      "1\t0.6285601332087014\t0.1108453191011763\t0.12065840477889567\t0.13993614291122658\n",
      "2\t0.10533739392051032\t0.0275965813340302\t0.7941429715323356\t0.07292305321312387\n",
      "3\t0.0\t0.0\t1.0\t0.0\n",
      "...\n",
      "pos\tA\tC\tG\tU\n",
      "0\t0.32465600595016736\t0.3589800287457409\t0.18931315100894872\t0.12705081429514303\n",
      "1\t0.6285601332087014\t0.1108453191011763\t0.12065840477889567\t0.13993614291122658\n",
      "2\t0.10533739392051032\t0.0275965813340302\t0.7941429715323356\t0.07292305321312387\n",
      "3\t0.0\t0.0\t1.0\t0.0\n",
      "4\t0.0\t0.028156084682674495\t0.0\t0.9718439153173255\n",
      "5\t0.5726332505804429\t0.03857557818137959\t0.3478000127312139\t0.04099115850696364\n",
      "6\t0.6664756984578479\t0.08507466186900921\t0.12876617785505848\t0.11968346181808436\n",
      "7\t0.0921907403870959\t0.06590413395917301\t0.7578891647318572\t0.0840159609218739\n",
      "8\t0.17763728771538367\t0.15625889928604692\t0.19366186565889057\t0.4724419473396788\n",
      "\n"
     ]
    }
   ],
   "source": [
    "### Generate 5'ss probability matrix\n",
    "\n",
    "description = \"\"\"\n",
    "Probability matrix for all 5'ss sequences in the human genome. \n",
    "Sequences were parsed from hg38 using GENCODE annotation. \n",
    "Only sequences of the form NNNGYNNNN were considered.  \n",
    "\n",
    "References\n",
    "\n",
    "Frankish A et al. (2019) GENCODE reference annotation \n",
    "for the human and mouse genomes. Nucl Acids Res. 47(D1):D766–73. \n",
    "\"\"\"\n",
    "\n",
    "# load sequence dataframe\n",
    "with logomaker.open_example_datafile('ss_sequences.txt', print_description=False) as f:\n",
    "    seq_df = pd.read_csv(f, delim_whitespace=True, comment='#')\n",
    "\n",
    "# compute probability matrix\n",
    "prob_df = logomaker.alignment_to_matrix(sequences=seq_df['sequence'], \n",
    "                                        counts=seq_df['count'],\n",
    "                                        to_type='probability',\n",
    "                                        pseudocount=0)\n",
    "\n",
    "# save probability matrix\n",
    "write_df_and_description(df=prob_df, \n",
    "                         description=description, \n",
    "                         file_name='ss_probability_matrix.txt', \n",
    "                         index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Description of example matrix \"ss_probability_matrix\":\n",
      "# \n",
      "# Probability matrix for all 5'ss sequences in the human genome.\n",
      "# Sequences were parsed from hg38 using GENCODE annotation.\n",
      "# Only sequences of the form NNNGYNNNN were considered.\n",
      "# \n",
      "# References\n",
      "# \n",
      "# Frankish A et al. (2019) GENCODE reference annotation\n",
      "# for the human and mouse genomes. Nucl Acids Res. 47(D1):D766–73.\n",
      "# \n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<logomaker.src.Logo.Logo at 0x1160eb9b0>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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p4dh1rNqTkQFPPw1/+hNkZcGKFfJYs8Z6SYzOGDoUZs2COXPg3HMhwWBngoqG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\ncNKYwZUVAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x180 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Validate 5'ss matrix\n",
    "df = logomaker.get_example_matrix('ss_probability_matrix')\n",
    "logomaker.Logo(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ARS wt sequence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-> saving data to ../logomaker/examples/datafiles/ars_wt_sequence.txt:\n",
      "# \n",
      "# ARS1 (a.k.a. ARS416) wild-type sequence.\n",
      "# On S. cerevisiae S288C chromosome IV\n",
      "# Sequence ID: NC_001136.10\n",
      "# position: [462475:462624] (150 bp)\n",
      "# strand: minus\n",
      "# \n",
      "ATCTTGTTATTTTACAGATTTTATGTTTAGATCTTTTATGCTTGCTTTTCAAAAGGCCTGCAGGCAAGTGCACAAACAATACTTAAATAAATACTACTCAGTAATAACCTATTTCTTAGCATTTTTGACGAAATTTGCTATTTTGTTAGA...\n",
      "# \n",
      "# ARS1 (a.k.a. ARS416) wild-type sequence.\n",
      "# On S. cerevisiae S288C chromosome IV\n",
      "# Sequence ID: NC_001136.10\n",
      "# position: [462475:462624] (150 bp)\n",
      "# strand: minus\n",
      "# \n",
      "ATCTTGTTATTTTACAGATTTTATGTTTAGATCTTTTATGCTTGCTTTTCAAAAGGCCTGCAGGCAAGTGCACAAACAATACTTAAATAAATACTACTCAGTAATAACCTATTTCTTAGCATTTTTGACGAAATTTGCTATTTTGTTAGA\n"
     ]
    }
   ],
   "source": [
    "### ARS wt sequence\n",
    "\n",
    "description = \"\"\"\n",
    "ARS1 (a.k.a. ARS416) wild-type sequence.\n",
    "On S. cerevisiae S288C chromosome IV\n",
    "Sequence ID: NC_001136.10\n",
    "position: [462475:462624] (150 bp)\n",
    "strand: minus\n",
    "\"\"\"\n",
    "\n",
    "# define reverse complement function\n",
    "def rc(seq):\n",
    "    \"\"\"Reverse-complements a DNA sequence\"\"\"\n",
    "    complement = str.maketrans('ATCGN', 'TAGCN')\n",
    "    return str(seq).upper().translate(complement)[::-1]\n",
    "\n",
    "# define sequence\n",
    "ars416 = rc(\n",
    "    'tctaacaaaatagcaaatttcgtcaaaaatgctaagaaataggtt'\n",
    "    'attactgagtagtatttatttaagtattgtttgtgcacttgcctg'\n",
    "    'caggccttttgaaaagcaagcataaaagatctaaacataaaatct'\n",
    "    'gtaaaataacaagat'.upper())\n",
    "\n",
    "# write to file\n",
    "write_txt_and_description(txt=ars416, \n",
    "                          description=description, \n",
    "                          file_name=data_dir+'ars_wt_sequence.txt')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ARS selection data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-> saving data to ../logomaker/examples/datafiles/ars_sequences.txt.gz:\n",
      "# \n",
      "# ARS1 selection data (unpublished).\n",
      "# Collected by JBK from an experiment analogous to the\n",
      "# arsMut-seq assay described by Liachko et al. (2013).\n",
      "# \n",
      "# Reference:\n",
      "# \n",
      "# Liachko I et al. (2013) High-resolution mapping, characterization, and optimization\n",
      "# of autonomously replicating sequences in yeast. Genome Res. 23(4):698–704.\n",
      "# \n",
      "library_ct\tselected_ct\tsequence\n",
      "666\t0\tTCTAACAAAATAGCAAATTTCGTCAAAAATGCTAAGAAATGGGTTATTATTGGGTAGTATTTATGTGAGTCTTGTTCGTGCACTTGCCCGCAGGCATGTTGCAAACCAAGCATAAAAGATCTAAACATAGAATCTGTCAAATAACCAGAT\n",
      "292\t0\tTCTAACAAAATAGCAAATTTCGTCGAAAATGTTTAGAAAAAGGTTATTACTGAGTAGTATTTATTTATGTATTGATTGTGCAATTGCCTGGAGGCCTTTTGAAACGAAAGCATAAAAGATCTAAACATAAACTCTGTAAAATAACAAGAT\n",
      "224\t0\tTCTAACAAAATAGCAAATTTCGTCAAAAATGCAATGAAAGCGGATATTACTTAGTGGTATTTATTTAAGTATTGTTTGTGCACTTGCCTGCAGGCCTTTAGAATAGCAAGCATAAAAGACCTAAACATAAAATCTGTAAAATAACAAGAT\n",
      "188\t0\tTCTAACAAAATAGCAAATTTCGGCAAAAATGCTAAGAAATAGGTTATTACAGAGTAGAATTTATGTACGTATTGTTTGTGCAATTGCCTGCAGGCCTTTTGGAAAGCAAGCATAAAAGTTATAAACATAGATTCTGTAAAATAACATGAT\n",
      "...\n",
      "0\t2\tTCTAACAAAATAGCAAATTTCGTCAAAAATGCTAAGAAATAGGTTATTACTGAGTAGTATTTATTTAAGTATTGTTTATACACTTGCCTGCAGGCTTTTTGAAAAGCAAGCATAAAAGATCTAAACATAAAATCTGTAAAATAACAAGCT\n",
      "0\t2\tTCTAACAAAATAGCAAATTTCGTCAAAAATGCTAAGAAATTGGCTATTACCGAGTAGTATCTATTTAAGTATTGGTGGTGAACCTGCCTGCAGGCCTTTTGAAAAGCAAGCATAAAAGATCTAAACATAAAATCTATAAAATAACAAGAT\n",
      "0\t2\tTCTAACAAAATAGCAAATTTCGTCAAAAATTCTAAGAAATAGGTTATTACTGAGTAGTATTTATTTAAGTGTTGTTGGTGCACTAGCCATCAGGCCTTTTGAAAAGCAAGCTTAAAGGATCTAAACATAAAATCTGTAAAAGAACACGAT\n",
      "0\t2\tTCTAACAAAATAGCAAATTTCGTCAAAAATGCTAAGAAATAGGTTATTACTGAGTTGTATTTGTTTAAGAATTGTTAGTGCATGTGCCTGCAGGCCTTTTGAAAAGCAAGCCTAAAAGATCTAAACATAAAATCTGTTAAATAACAAGAT\n",
      "0\t2\tTCTAACAAAATAGCAAATTTCGCCAAAAATGCTAAGAGAAAGGTTATTACTGAGTAGTATTTATTTAAGTGTTGTTTGTGCACTTGCCTGCAGGCCTTTTGAAAAGCAGGCATAAAAGATCTAAACATAAAATCTGTAAAATTACAAGTT\n",
      "0\t2\tTCTAACAAAATAGCAAATTTCGTCAAAAATGGCGAGAAATCGGTTATTACTGTGTAGTATTAGTGTAAGTAGTGTTTGTGCTCTTGCCTGCAGGCCTTTTGAAAAGCAGGCATAAAAGATCTAAACATAAAATCTGTAAAATTACAAGTT\n",
      "0\t2\tTCTAACAAAATAGCAAATTTCGTCTACACTGCTAAGAAATAGGTTATTACTGAGTCGTATGAATTTAAGTATGGTTTGAGCACTTGCCTGCATGCCTTTTGAAAAGCAAGCATGAAAGATCTAAACATAAAATATGCAAAATAACAACAT\n",
      "0\t2\tTCTAACAAAATAGCAAATTTCGGCAAAAACGTTAAGGAATAGGTTATTAATGAGTTGTATTTATTTAAGTATTGTTTGTGCACTTGCCTGCAGGCCTATTGAAAAGCAAGCATTAAAGATCTAAACGTAAAATCTGAAAAATACCATGAT\n",
      "0\t2\tTCTAACAAAATAGCAAATTTCGTCAAAAGTGGTAAGAAATAGGTTATTAGTGAGTAGTATTTAATTACGTATTGTTTGTTCAATTGCCTGCAGGCCTTTTGAAAAGTAGGCATAAAAGAACGAAACATAAAATCTGTAATATAACAAGAT\n",
      "0\t2\tTCTAACAAAATAGCAAATTTCGTGATAACTGTTATGAAATAGGTTATTACTGAATAGTATTTATTCAAGTATTGTTCGTGCACTTGCCTGCAGGCCTTTTGAAAAGCAAGCATAAAAGATCTAAACATAAAATCTGCAAAATGACAAGAT\n",
      "\n",
      "Compressing file...\n",
      "Done!\n"
     ]
    }
   ],
   "source": [
    "### Format ARS1 data\n",
    "def get_ars_df(file_name):\n",
    "    \n",
    "    # open file\n",
    "    with gzip.open(file_name, 'r') as f:\n",
    "        txt = [x.decode(\"utf-8\") for x in f]\n",
    "    \n",
    "    # extract counts and sequences\n",
    "    counts = np.array([int(str(name).strip().split('-')[-1]) for name in txt if '>' in name])\n",
    "    seqs = [str(s).strip() for s in txt if '>' not in s]\n",
    "    \n",
    "    # format as dataframe\n",
    "    df = pd.DataFrame()\n",
    "    df['sequence'] = seqs\n",
    "    df['count'] = counts\n",
    "    return df\n",
    "\n",
    "# load both background and forground data\n",
    "bg_df = get_ars_df('ARS416_09_B1.qcfilt.aln.full.gz')\n",
    "fg_df = get_ars_df('ARS416_09_B2.qcfilt.aln.full.gz')\n",
    "\n",
    "# merge and format ars data\n",
    "ars_df = pd.merge(bg_df, fg_df, on='sequence', how='outer').fillna(0)\n",
    "ars_df.rename(columns={'count_x':'library_ct', 'count_y':'selected_ct'}, inplace=True)\n",
    "ars_df = ars_df[['library_ct', 'selected_ct', 'sequence']]\n",
    "ars_df.iloc[:,[0,1]] = ars_df.iloc[:,[0,1]].astype(int)\n",
    "\n",
    "# write description\n",
    "description = \"\"\"\n",
    "ARS1 selection data (unpublished).\n",
    "Collected by JBK from an experiment analogous to the \n",
    "arsMut-seq assay described by Liachko et al. (2013). \n",
    "\n",
    "Reference:\n",
    "\n",
    "Liachko I et al. (2013) High-resolution mapping, characterization, and optimization \n",
    "of autonomously replicating sequences in yeast. Genome Res. 23(4):698–704. \n",
    "\"\"\"\n",
    "\n",
    "# write df to file\n",
    "write_df_and_description(df=ars_df, \n",
    "                         description=description, \n",
    "                         file_name=data_dir+'ars_sequences.txt.gz', \n",
    "                         index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-> saving data to ../logomaker/examples/datafiles/ars_sequences.txt.gz:\n",
      "# \n",
      "# ARS1 selection data (unpublished).\n",
      "# Collected by JBK from an experiment analogous to the\n",
      "# arsMut-seq assay described by Liachko et al. (2013).\n",
      "# \n",
      "# Reference:\n",
      "# \n",
      "# Liachko I et al. (2013) High-resolution mapping, characterization, and optimization\n",
      "# of autonomously replicating sequences in yeast. Genome Res. 23(4):698–704.\n",
      "# \n",
      "library_ct\tselected_ct\tsequence\n",
      "666\t0\tTCTAACAAAATAGCAAATTTCGTCAAAAATGCTAAGAAATGGGTTATTATTGGGTAGTATTTATGTGAGTCTTGTTCGTGCACTTGCCCGCAGGCATGTTGCAAACCAAGCATAAAAGATCTAAACATAGAATCTGTCAAATAACCAGAT\n",
      "292\t0\tTCTAACAAAATAGCAAATTTCGTCGAAAATGTTTAGAAAAAGGTTATTACTGAGTAGTATTTATTTATGTATTGATTGTGCAATTGCCTGGAGGCCTTTTGAAACGAAAGCATAAAAGATCTAAACATAAACTCTGTAAAATAACAAGAT\n",
      "224\t0\tTCTAACAAAATAGCAAATTTCGTCAAAAATGCAATGAAAGCGGATATTACTTAGTGGTATTTATTTAAGTATTGTTTGTGCACTTGCCTGCAGGCCTTTAGAATAGCAAGCATAAAAGACCTAAACATAAAATCTGTAAAATAACAAGAT\n",
      "188\t0\tTCTAACAAAATAGCAAATTTCGGCAAAAATGCTAAGAAATAGGTTATTACAGAGTAGAATTTATGTACGTATTGTTTGTGCAATTGCCTGCAGGCCTTTTGGAAAGCAAGCATAAAAGTTATAAACATAGATTCTGTAAAATAACATGAT\n",
      "...\n",
      "0\t2\tTCTAACAAAATAGCAAATTTCGTCAAAAATGCTAAGAAATAGGTTATTACTGAGTAGTATTTATTTAAGTATTGTTTATACACTTGCCTGCAGGCTTTTTGAAAAGCAAGCATAAAAGATCTAAACATAAAATCTGTAAAATAACAAGCT\n",
      "0\t2\tTCTAACAAAATAGCAAATTTCGTCAAAAATGCTAAGAAATTGGCTATTACCGAGTAGTATCTATTTAAGTATTGGTGGTGAACCTGCCTGCAGGCCTTTTGAAAAGCAAGCATAAAAGATCTAAACATAAAATCTATAAAATAACAAGAT\n",
      "0\t2\tTCTAACAAAATAGCAAATTTCGTCAAAAATTCTAAGAAATAGGTTATTACTGAGTAGTATTTATTTAAGTGTTGTTGGTGCACTAGCCATCAGGCCTTTTGAAAAGCAAGCTTAAAGGATCTAAACATAAAATCTGTAAAAGAACACGAT\n",
      "0\t2\tTCTAACAAAATAGCAAATTTCGTCAAAAATGCTAAGAAATAGGTTATTACTGAGTTGTATTTGTTTAAGAATTGTTAGTGCATGTGCCTGCAGGCCTTTTGAAAAGCAAGCCTAAAAGATCTAAACATAAAATCTGTTAAATAACAAGAT\n",
      "0\t2\tTCTAACAAAATAGCAAATTTCGCCAAAAATGCTAAGAGAAAGGTTATTACTGAGTAGTATTTATTTAAGTGTTGTTTGTGCACTTGCCTGCAGGCCTTTTGAAAAGCAGGCATAAAAGATCTAAACATAAAATCTGTAAAATTACAAGTT\n",
      "0\t2\tTCTAACAAAATAGCAAATTTCGTCAAAAATGGCGAGAAATCGGTTATTACTGTGTAGTATTAGTGTAAGTAGTGTTTGTGCTCTTGCCTGCAGGCCTTTTGAAAAGCAGGCATAAAAGATCTAAACATAAAATCTGTAAAATTACAAGTT\n",
      "0\t2\tTCTAACAAAATAGCAAATTTCGTCTACACTGCTAAGAAATAGGTTATTACTGAGTCGTATGAATTTAAGTATGGTTTGAGCACTTGCCTGCATGCCTTTTGAAAAGCAAGCATGAAAGATCTAAACATAAAATATGCAAAATAACAACAT\n",
      "0\t2\tTCTAACAAAATAGCAAATTTCGGCAAAAACGTTAAGGAATAGGTTATTAATGAGTTGTATTTATTTAAGTATTGTTTGTGCACTTGCCTGCAGGCCTATTGAAAAGCAAGCATTAAAGATCTAAACGTAAAATCTGAAAAATACCATGAT\n",
      "0\t2\tTCTAACAAAATAGCAAATTTCGTCAAAAGTGGTAAGAAATAGGTTATTAGTGAGTAGTATTTAATTACGTATTGTTTGTTCAATTGCCTGCAGGCCTTTTGAAAAGTAGGCATAAAAGAACGAAACATAAAATCTGTAATATAACAAGAT\n",
      "0\t2\tTCTAACAAAATAGCAAATTTCGTGATAACTGTTATGAAATAGGTTATTACTGAATAGTATTTATTCAAGTATTGTTCGTGCACTTGCCTGCAGGCCTTTTGAAAAGCAAGCATAAAAGATCTAAACATAAAATCTGCAAAATGACAAGAT\n",
      "\n",
      "Compressing file...\n",
      "Done!\n"
     ]
    }
   ],
   "source": [
    "# write df to file\n",
    "write_df_and_description(df=ars_df, \n",
    "                         description=description, \n",
    "                         file_name=data_dir+'ars_sequences.txt.gz', \n",
    "                         index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ARS enrichment matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-> saving data to ../logomaker/examples/matrices/ars_enrichment_matrix.txt:\n",
      "# \n",
      "# ARS1 enrichment matrix.\n",
      "# From unpublished data collected by JBK, from an experiment analogous\n",
      "# to the arsMut-seq assay described by Liachko et al. (2013).\n",
      "# \n",
      "# Reference:\n",
      "# \n",
      "# Liachko I et al. (2013) High-resolution mapping, characterization, and optimization\n",
      "# of autonomously replicating sequences in yeast. Genome Res. 23(4):698–704.\n",
      "# \n",
      "pos\tA\tC\tG\tT\n",
      "0\t0.06289044538461087\t-0.17457791973116166\t-0.08827744456839261\t0.1999649189149434\n",
      "1\t0.5053118843452795\t-0.38052071601400184\t-0.09930254597241894\t-0.025488622358858775\n",
      "2\t0.3249559382230771\t-0.2236892757859591\t-0.22238592467840845\t0.12111926224129044\n",
      "3\t-0.06275561844795949\t-0.11743157592129255\t0.004755662058099047\t0.17543153231115294\n",
      "...\n",
      "140\t0.7004668568139971\t-0.04671635746357278\t-1.1534011786514304\t0.49965067930100626\n",
      "141\t-0.22560653908605502\t0.014853017592215578\t0.3915989094062299\t-0.1808453879123905\n",
      "142\t-0.23738657625029602\t-0.007163404794385431\t-0.018703251297139734\t0.26325323234182135\n",
      "143\t1.2383239511775723\t-0.16199138541743455\t-1.0028045353317083\t-0.07352803042842945\n",
      "144\t-0.07576970953445356\t0.2729452313280518\t0.31062066585252335\t-0.5077961876461217\n",
      "145\t0.14161956765466965\t0.00529418375530083\t-0.21245251002671922\t0.06553875861674871\n",
      "146\t0.22477180759846016\t-0.2514310233629503\t-0.14158582837385975\t0.16824504413834984\n",
      "147\t0.03811096718183665\t0.3399607658487469\t-0.1370335587772225\t-0.24103817425336108\n",
      "148\t-0.40393337347221225\t0.04366568542901615\t0.1472762488910034\t0.21299143915219265\n",
      "149\t0.24712371051316395\t-0.24162476030950752\t-0.09121024662587113\t0.08571129642221467\n",
      "\n"
     ]
    }
   ],
   "source": [
    "## Compute ARS matrix\n",
    "\n",
    "# set description\n",
    "description = \"\"\"\n",
    "ARS1 enrichment matrix.\n",
    "From unpublished data collected by JBK, from an experiment analogous\n",
    "to the arsMut-seq assay described by Liachko et al. (2013). \n",
    "\n",
    "Reference:\n",
    "\n",
    "Liachko I et al. (2013) High-resolution mapping, characterization, and optimization \n",
    "of autonomously replicating sequences in yeast. Genome Res. 23(4):698–704. \n",
    "\"\"\"\n",
    "\n",
    "# load ars selection data\n",
    "with logomaker.open_example_datafile('ars_sequences.txt.gz', print_description=False) as f:\n",
    "    ars_df = pd.read_csv(f, sep='\\t', comment='#')\n",
    "\n",
    "# compute probability matrices from alignments\n",
    "bg_df = logomaker.alignment_to_matrix(sequences=ars_df['sequence'], \n",
    "                                      counts=ars_df['library_ct'], \n",
    "                                      to_type='probability')\n",
    "fg_df = logomaker.alignment_to_matrix(sequences=ars_df['sequence'], \n",
    "                                      counts=ars_df['selected_ct'], \n",
    "                                      to_type='probability')\n",
    "\n",
    "# compute enrichment matrix, center, and reverse-complement\n",
    "enrichment_df = np.log2(fg_df/bg_df)\n",
    "enrichment_df = logomaker.transform_matrix(enrichment_df, center_values=True)\n",
    "enrichment_df.loc[:,:] = enrichment_df.values[::-1,::-1]\n",
    "\n",
    "# save enrichment matrix\n",
    "write_df_and_description(df=enrichment_df, \n",
    "                         description=description,\n",
    "                         file_name=mat_dir+'ars_enrichment_matrix.txt',\n",
    "                         index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Description of example matrix \"ars_enrichment_matrix\":\n",
      "# \n",
      "# ARS1 enrichment matrix.\n",
      "# From unpublished data collected by JBK, from an experiment analogous\n",
      "# to the arsMut-seq assay described by Liachko et al. (2013).\n",
      "# \n",
      "# Reference:\n",
      "# \n",
      "# Liachko I et al. (2013) High-resolution mapping, characterization, and optimization\n",
      "# of autonomously replicating sequences in yeast. Genome Res. 23(4):698–704.\n",
      "# \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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RPFkqVHX+SE8WkKfjYM5B1MFqigqKGmfgggbz7vR3ie0dW/34wxkf8tWCr2q1KygrILhl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\nqszMzMzawEWVmZmZWRv8D5z2zBkU28CPAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x180 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Validate ARS1 enrichment matrix\n",
    "df = logomaker.get_example_matrix('ars_enrichment_matrix')\n",
    "logo = logomaker.Logo(df)\n",
    "logo.style_glyphs_in_sequence(sequence=ars416, color='black')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "anaconda_kernel",
   "language": "python",
   "name": "anaconda_kernel"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
